In this section we report the experiments and the results that we performed to test REM. We first show the results on the MNIST dataset, reporting also how the entropy and the accuracy values change during training. Then, we test REM on more complex datasets such as Fashion-MNIST, CIFAR10 and Tiny ImageNet. We also performed experiments to test the robustness to affine transformations of CapsNets+REM. Except for Tiny ImageNet, we used the same architectures configurations and augmentations described in [1]}.we have removed the decoder part of the network, see Sec. REF  for more details.
We trained models with five random seeds. We report the classification accuracy (%) and entropy (averages and standard deviations), the sparsity (percentage of pruned parameters, median) and the number of keys in the dictionary (median).
The code will be open-source released upon acceptance of the paper, to guarantee the reproducibility of the results. The experiments were run on a Nvidia Ampere A40 equipped with 48GB RAM, and the code uses PyTorch 1.9.
